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Record W3008822835 · doi:10.1093/jcag/gwz047.145

A146 MANAGEMENT OF ANTITHROMBOTIC THERAPY AFTER GASTROINTESTINAL BLEEDING: A MIXED METHODS STUDY OF HEALTHCARE PROVIDERS

2020· article· en· W3008822835 on OpenAlexaff
Derek Little, Tara Pinto, James D. Douketis, Joanna C. Dionne, Anne Holbrook, Ted Xenodemetropoulos, Deborah Siegal

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal Bleeding Diagnosis and Treatment
Canadian institutionsMcMaster UniversityPopulation Health Research InstituteUniversity of Toronto
Fundersnot available
KeywordsMedicineHealth careDiscontinuationBleedDescriptive statisticsFocus groupPreferenceFamily medicineSurgeryStatistics

Abstract

fetched live from OpenAlex

Abstract Background Oral anticoagulants (OAC) are permanently discontinued in up to 50% of patients after gastrointestinal (GI) bleeding despite ongoing thrombotic risk and evidence of benefit to restarting. The reasons for permanent discontinuation of OAC are unclear, but likely include concerns about re-bleeding and a lack of high-quality evidence. There are no studies evaluating healthcare provider values and preferences following OAC-related GI bleeding and their influence on decision-making about whether and when to resume OACs. Aims We aimed to (i) identify key factors (attributes) that influence healthcare provider decision-making regarding resumption of OAC after GI bleeding, (ii) determine the relative importance of these attributes, and (iii) to identify preference groups. Methods We conducted focus group discussions (FGD) with healthcare providers involved in the care of patients with OAC-related GI bleeding. Participants ranked the relative importance of the attributes identified during the FGD through a dot voting exercise. Transcripts were reviewed and themes (attributes) were identified. Results of the FGD informed a discrete choice experiments survey developed and analyzed using the Sawtooth software platform (Sawtooth Software, USA). The survey was pilot tested and modified by iterative feedback. A sample choice task is shown in Image 1. Hierarchical Bayes analysis was used to estimate preference coefficients (utilities) for each attribute. Latent class analysis was used to identify preference groups. Results There were 4 FGD involving 29 participants. The most important attributes identified and included in the survey were thrombosis risk, indication for OAC, index bleed severity, re-bleeding risk, and patient characteristics. There were 130 survey respondents practicing in hematology (n=68), internal medicine (n=30), gastroenterology (n=7), cardiology (n=4), family medicine (n=3), and others (n=18). The mean age was 45 years (±11) and 51% were male. Thrombosis risk and re-bleeding risk equally had the highest utility followed by index bleed severity, patient characteristics, and indication for OAC. Two preference groups were identified. The dominant preference group (78% of respondents) placed the highest utility on thrombosis risk and re-bleeding risk, while a minority (22% of respondents) placed the highest utility on index bleed severity. Conclusions Thrombosis risk and re-bleeding risk are equally the most important factors influencing OAC resumption following OAC-related GI bleeding. The severity of the index bleed is the most important factor in decision-making for a minority segment of healthcare providers. Further research on the dose, type and timing of OAC resumption is needed to determine the optimal balance between thrombosis and re-bleeding. Funding Agencies Heart and Stroke Foundation

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.310
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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